Frequency spectrum usage for wireless communications is in most instances statically allocated to individual services (e.g., TV stations, radar, and police enforcement) by regulatory bodies. As a result of such static spectrum allocation many frequency bands are under-exploited both in time and in space.
Current worldwide research is defining a framework to allow devices to temporarily and dynamically use such under-exploited frequency bands without harming licensed users. Spectrum allocation and usage policies allowed should be delivered, i.e. made available, with spectrum agile radios to prevent uncontrolled terminal behavior. Recently, a new wireless research area has emerged, referred to as cognitive radio as described in J. Mitola, “Software Radio-Cognitive Radio, Wireless Architectures for the 21st Century”, as disclosed at http://ourworld.compuserve.com/homepages/jmitola.
A cognitive radio (CR) is able to sense the frequency spectrum and use bands that are not being utilized by their licensed primary users. In so doing, a cognitive radio should minimize interference with its neighbors and, most importantly, should make sure to render the frequency band immediately free and available to such primary users as soon as they re-appear.
This research area is now being endorsed by regulatory bodies, like the U.S. Federal Communications Commission (FCC) that started investigating the consequences of such changes in the spectrum usage. Specific programs have been started, for example by the Defense Advanced Research Projects Agency (DARPA), with the aim of creating the technological and normative conditions that enable dynamic spectrum allocation and usage by frequency-agile terminals.
Cognitive radio concepts are thus moving out of a pure research field and are being endorsed by regulatory and standardization bodies. For instance, in the US some old analog TV bands are being made available for experimentation of frequency-agile systems, and IEEE802.22 is developing suitable protocols to specify cognitive radio behavior in those frequencies. Another area of potential application is Wireless-LAN, where frequency agile behavior according to a cognitive radio paradigm may take place within the ISM band.
Specifically, a spectrum allocation policy language is being defined by the DARPA XG Working Group, “XG Policy Language Framework”, version 1.0, April 2004 to describe the terms dictated by a regulator for dynamic frequency access that mobile terminals should abide. By means of this language, wireless terminals receive a description of the bands which can be (temporarily) used, and the conditions for such usages. Rules may change depending on both physical location and time.
Recently, IEEE has also started work at standardizing the basic parameters related to the deployment of spectrum agile systems in frequency bands previously allocated to analog TV stations, as in IEEE P802.22, “Standard for Wireless Regional Area Networks (WRAN)-Specific requirements-Part 22: Cognitive Wireless RAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: Policies and Procedures for Operation in the TV Bands.”
U.S. published patent application no. 2004/0092281 describes an approach that increases the available spectrum in a wireless network by sharing existing allocated (and in-use) portions of the radio frequency (RF) spectrum in a manner that will minimize the probability of interfering with existing legacy users. The approach described in this document provides interference adaptive waveforms, and a variety of physical and media access control protocols for generating waveforms based on measurement and characterization of the local spectrum.
This prior approach measures the local spectrum at a receiving node, generates an optimal waveform profile specifying transmission parameters that will water-fill the unused spectrum up to an interference limit without causing harmful interference to primary and legacy transmitters using the same frequency bands, and enables simultaneous transmit and receive modes at a multiplicity of transceivers in a wireless network. The related arrangement also provides closed loop feedback control between nodes, co-site interference management, intersymbol interference mitigation, wide sense stationary base-band signaling and modulation, and power limited signaling for avoiding detection and interception.
U.S. published patent application 2004/0047324 describes a system and a method for managing communication with a plurality of wireless client devices operating in a radio frequency band shared by other types of devices. The managing comprises controlling at least one parameter associated with radio communication with each of the plurality of wireless devices based on the radio frequency environment associated with each corresponding one of the plurality of wireless client devices. Spectrum profile information describing the radio frequency environment (activity in the frequency band) at a wireless client device is sent to the wireless base station device (where the parameter controls are made) from either a wireless client device or another radio device in the proximity of one or more wireless client devices that is capable of generating the spectrum profile information.
The spectrum profile information may include information identifying signals that are occurring in the frequency band in the proximity of a wireless client device. Examples of parameters that may be controlled at the wireless base station device include packet fragmentation threshold (the length of a data packet), transmission data rate and transmission scheduling (synchronizing a transmission to quiescent intervals of one or more periodic interfering signals).
U.S. patent application no. 2004/0171390 describes classes of cognition models which may include radio environment models, mobility models, and application/user context models. These models are utilized in a wireless communications network. Radio environment models represent the physical aspects of the radio environment, such as shadowing losses, multi-path propagation, interference and noise levels, etc. Mobility models represent users motion in terms of geo-coordinates and/or logical identifiers, such as street names, as well as speed of a user terminal. The context model represents the present state and dynamics of each of these application processes within itself and between multiple application processes. These data are employed to optimize network performance.